class_docstr: Upsampling layer for 1D inputs.
  
  Repeats each temporal step `size` times along the time axis.
  
  Examples:
  
  >>> input_shape = (2, 2, 3)
  >>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
  >>> x
  [[[ 0  1  2]
    [ 3  4  5]]
   [[ 6  7  8]
    [ 9 10 11]]]
  >>> y = keras.layers.UpSampling1D(size=2)(x)
  >>> y
  [[[ 0.  1.  2.]
    [ 0.  1.  2.]
    [ 3.  4.  5.]
    [ 3.  4.  5.]]
  
   [[ 6.  7.  8.]
    [ 6.  7.  8.]
    [ 9. 10. 11.]
    [ 9. 10. 11.]]]
  
  Args:
      size: Integer. Upsampling factor.
  
  Input shape:
      3D tensor with shape: `(batch_size, steps, features)`.
  
  Output shape:
      3D tensor with shape: `(batch_size, upsampled_steps, features)`.
code: def compute_output_shape(self, input_shape):
      size = (
          self.size * input_shape[1]
          if input_shape[1] is not None
          else None
      )
      return [input_shape[0], size, input_shape[2]]
  
file_path: keras/keras/layers/reshaping/up_sampling1d.py
imports: from keras import ops
  from keras.api_export import keras_export
  from keras.layers.input_spec import InputSpec
  from keras.layers.layer import Layer
label_desc: Use image node_id 2 for calling the UpSampling1D obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: list